1. Field
Embodiments of the present disclosure relate to a system and a method for detection of an abnormality based on analysis of time-series data and prediction of proliferation or diminishing of a detected abnormality.
2. Discussion of Related Art
As most work in companies or government agencies is being computerized, establishment and management of large-scale enterprise service infrastructures by such companies have been made a matter of routine. Such enterprise service infrastructures include a number of web servers, web application servers (WAS), databases, firewalls, switches, routers, and the like. Since there are cases in which an abnormality can affect other devices, or even an entire system, a consistent status monitoring system is needed.
In order to realize such a monitoring system, conventionally, status information (i.e., a central processing unit (CPU) usage rate, a memory usage rate, storage device status information, etc.) has been collected from each device, and the monitoring system has been accomplished by notifying an administrator when collected information has exceeded a preset threshold value. However, since such a conventional method involves determining a result simply by checking whether or not acquired data exceeds a threshold value, there is a limitation on predicting how an abnormality that has occurred will progress.